1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
19/12/2023 Aporte Basureros 10000 Tami NA
22/12/2023 Netflix 8326 Tami NA
22/12/2023 Diosi 14700 Andrés Antiparasitario
24/12/2023 Comida 79633 Tami Supermercado
24/12/2023 Comida 19950 Andrés Empanadas
25/12/2023 Uber 5595 Tami NA
27/12/2023 Diosi 22970 Andrés arena
30/12/2023 Comida 50000 Andrés jumbo la litad de los 97
2/1/2024 Electricidad 55466 Andrés pac enel
2/1/2024 Comida 44542 Tami Supermercado
5/1/2024 Comida 9516 Tami Burger King pre Maite
5/1/2024 Comida 19590 Tami Barritas Soul
8/1/2024 Comida 57625 Tami Supermercado
13/1/2024 Limpieza alfombras 60000 Tami NA
13/1/2024 Comida 33110 Andrés NA
15/1/2024 Comida 98241 Tami Supermercado
17/1/2024 VTR 21990 Andrés NA
22/1/2024 Netflix 8304 Tami NA
22/1/2024 Comida 44016 Tami Supermercado
23/1/2024 Comida 7500 Andrés NA
27/1/2024 Jardinero 40000 Tami NA
29/1/2024 Comida 65786 Tami Supermercado
30/1/2024 Electricidad 55759 Andrés NA
3/2/2024 donación 50000 Andrés NA
4/2/2024 Comida 46309 Andrés NA
5/2/2024 Comida 33079 Tami Supermercado
8/2/2024 Enceres 35440 Andrés casaideas
18/2/2024 VTR 21990 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.6529e+08   2    8.1732  3e-04 ***
## lag_depvar    9.7333e+10   1 1838.7388 <2e-16 ***
## Residuals     3.5360e+10 668                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   942.4045 13515.27 0.0193841
## 2-0 29633.255 23930.5965 35335.91 0.0000000
## 2-1 22404.417 19051.6989 25757.13 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
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## 29   28706.00             0   28640.00
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## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
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## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
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## 252  44028.00             2   38081.43
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## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
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## 273  61634.57             2   54746.29
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## 285  69120.71             2   62790.29
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## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
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## 291  52891.00             2   74644.71
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## 299  84485.00             2   83720.71
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## 309  46423.71             2   42298.86
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## 311  36435.14             2   37898.00
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## 313  34541.86             2   30209.57
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## 341  36489.29             2   35729.86
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## 346  41633.57             2   41917.86
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## 404  47996.14             2   56533.29
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## 406  45292.00             2   47207.57
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## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
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## 425  32611.57             2   26212.43
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## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
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## 432  35296.14             2   39328.29
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## 439  42020.86             2   34916.86
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## 441  37966.43             2   38303.00
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## 443  38988.14             2   41408.14
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## 448  39518.29             2   26517.00
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## 450  45623.14             2   39153.71
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## 452  41027.71             2   40627.43
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## 454  47139.43             2   42882.86
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## 456  41099.00             2   35547.57
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## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
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## 474  55149.57             2   46605.57
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## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
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## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
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## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
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## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
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## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
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## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
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## 515  34282.57             2   42479.14
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## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   516 51867.52 15344.241
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1921.87943   3998.80362   -496.77448   2473.87271  -2887.96856    548.42835 
##            8            9           10           11           12           13 
##  -5612.20655  -1236.97156  -4020.42064   -523.93896  -5030.55489  -1763.85535 
##           14           15           16           17           18           19 
##  -1053.18641    236.99940  -3350.31095   -517.99545  -2250.04178   6472.02915 
##           20           21           22           23           24           25 
##  -1523.69179  -1220.62905   1453.14834  -1172.32648    235.81134   1708.87094 
##           26           27           28           29           30           31 
##  -7052.25020    879.37620   8157.91176    536.30258    105.32047  -2287.55652 
##           32           33           34           35           36           37 
##   1643.17736   4666.81656   1296.04973   2567.42314  -1664.54781   4763.00636 
##           38           39           40           41           42           43 
##   4395.22261  -2121.57796  -2884.63420  -1075.43999 -10729.22606   7118.27969 
##           44           45           46           47           48           49 
##   2532.32676   1389.24598   8148.70637    862.66471   6695.42627   6971.72991 
##           50           51           52           53           54           55 
##  -5542.60879  -4597.73150  -4967.66161  -7933.30792   5991.72832  -4092.40176 
##           56           57           58           59           60           61 
##  -4976.64633   3701.44985    819.16454    -76.32222    103.76993  -5026.89822 
##           62           63           64           65           66           67 
##  18016.00436   3852.53977  -3398.33710   6080.73892   7581.30949  14971.85504 
##           68           69           70           71           72           73 
##   2233.77722 -12710.65536  -1090.90732   4810.29844  -4674.80604  -4289.83980 
##           74           75           76           77           78           79 
## -10471.10794   2312.70156  -5491.61126    892.83893  -6996.65670    317.43304 
##           80           81           82           83           84           85 
##  -2544.92124  -2899.22244  -4156.09553   -799.20488   2077.72218   3595.73959 
##           86           87           88           89           90           91 
##    395.55922   -546.82754    134.69041   4251.88176  -1133.10369   1158.00286 
##           92           93           94           95           96           97 
##  -2037.97279  -1055.38965    150.93351    255.25182  -7495.70367   2255.56896 
##           98           99          100          101          102          103 
##  -8680.14865  -3153.20899  -4274.05368  -2009.06974  -1527.67061   2927.88650 
##          104          105          106          107          108          109 
##  -2508.44010   2409.82657  -1274.61468    850.11607   2499.16672  -3186.63461 
##          110          111          112          113          114          115 
##  -4803.83942   -999.98369   1759.14258  11600.43300  -1125.19616   2750.85540 
##          116          117          118          119          120          121 
##   4380.77244   3678.74352   -885.93535  -4547.10153  -3655.21857   2317.76875 
##          122          123          124          125          126          127 
##  -1694.25850   1345.49355   8886.22749   1022.40173    298.82141  -2371.08200 
##          128          129          130          131          132          133 
##   2744.45508   7176.43629   1240.98859  -8281.71567   1796.30966   4207.11024 
##          134          135          136          137          138          139 
##  -3030.55316  -1355.93211   -821.45288  -3865.22657   1131.09297   -520.01921 
##          140          141          142          143          144          145 
##  -2942.62130   1644.29351  -1915.73146  -7890.62753   1854.12216  -3606.34575 
##          146          147          148          149          150          151 
##   1933.42894   -368.64738    922.25485   -429.31294   1285.66192   1152.11391 
##          152          153          154          155          156          157 
##   3347.23177  -4812.39890  -1212.86555  -3288.48198   5856.67788   9760.81270 
##          158          159          160          161          162          163 
##  -3626.59943  -5005.17872   3326.37108     14.47969   2543.12079  -5997.77858 
##          164          165          166          167          168          169 
##  -6922.36899   3891.17502  17231.20519   3772.91616   -212.35669  -2290.48138 
##          170          171          172          173          174          175 
##  -1009.28257   3654.58813   -113.47133  -7979.65166   2813.03853   4327.79209 
##          176          177          178          179          180          181 
##    698.13695   8823.20941  -9041.64003  -3444.77046 -10779.39075 -11448.89551 
##          182          183          184          185          186          187 
##    866.03051   8990.54756  -1542.50142   5805.37062   6542.82976  13251.24484 
##          188          189          190          191          192          193 
##   8721.81485  -3677.91344   2728.29354  10632.12907  -1252.30996  -2136.54934 
##          194          195          196          197          198          199 
## -10057.81597  -6341.13276   1145.03545  -5294.89000  -9938.37923   5097.68087 
##          200          201          202          203          204          205 
##  -3233.38702  -1909.10025  -1006.41666   6301.67053   9813.24782    669.96496 
##          206          207          208          209          210          211 
##   3007.70718   3211.30354   5927.02135  13044.89147  -5303.66125 -11058.11763 
##          212          213          214          215          216          217 
##  -5644.39432 -10662.98535  -5314.02371   1234.37934 -13243.89952  15975.12661 
##          218          219          220          221          222          223 
##   7706.39278   1557.99065  26733.47455  12982.98670   7924.41413  14649.48462 
##          224          225          226          227          228          229 
##  -3155.44581  -1149.49826   4258.76215    833.97938   3160.10327   9405.14924 
##          230          231          232          233          234          235 
##   6320.41485  -1387.63126  -1409.33729   9758.05503 -11072.06843  -7091.14983 
##          236          237          238          239          240          241 
##  -8494.19200 -10201.15876   2825.46315   1177.98821  -8430.73164  -9241.78402 
##          242          243          244          245          246          247 
##   8728.37746  -7943.20783   2205.19089 -10510.97578  -4404.19230   1049.48782 
##          248          249          250          251          252          253 
##    696.66271 -12570.95095   3221.67321   1748.71962   3966.55210   1982.81688 
##          254          255          256          257          258          259 
##  -1264.38056  11025.05385  20950.49469   3576.12742  -3899.88915   4357.83135 
##          260          261          262          263          264          265 
##  -1421.81094   3940.56755  -4623.63683 -10778.24703  -4805.51343   -669.11332 
##          266          267          268          269          270          271 
##  -5331.23203   8565.88659  -4336.19819   4066.31897  -2157.12916   4346.28649 
##          272          273          274          275          276          277 
##    695.54842   7293.47700  -1317.04380  12076.34465  -4366.10322   1833.37925 
##          278          279          280          281          282          283 
##   -262.49437   7930.87097  -4879.06154  -2662.84344 -11252.11362  -2839.45714 
##          284          285          286          287          288          289 
##  18459.29034   7886.94290   2931.29131   -426.47961   1061.84163   6537.02786 
##          290          291          292          293          294          295 
##   7088.69776 -18500.49722 -11190.78095  -8337.77024   9351.84102   2938.77801 
##          296          297          298          299          300          301 
##  -1255.35904  27311.89667  10383.99562   5316.53411   9941.63922   3355.21313 
##          302          303          304          305          306          307 
##   -566.79476   8276.52048 -23858.03656  -3516.68158   -215.84160  -7010.30830 
##          308          309          310          311          312          313 
##  -4113.60230   2748.47068  -9311.71747  -3469.13027  -8441.22063   1225.58183 
##          314          315          316          317          318          319 
##  -3423.77459   1765.23672  -4300.28881  27195.18788   -569.60756   3406.53896 
##          320          321          322          323          324          325 
##  10969.69576   5857.27930  32685.28530   5833.43616 -20243.23045   2088.55632 
##          326          327          328          329          330          331 
##   1387.30579  -6214.55081  -1614.35400 -33192.71625    505.93949  -2608.01845 
##          332          333          334          335          336          337 
##   -383.43310  -3413.61127   3834.67401   -590.86323  -7086.20298  -3323.86116 
##          338          339          340          341          342          343 
##  -2408.28857  -7891.10641   3567.78818  -1557.16688  -1912.04269  -1162.98291 
##          344          345          346          347          348          349 
##     22.37045    356.27150  -1715.20402  -9548.17903 -13425.02111   1945.27692 
##          350          351          352          353          354          355 
##  -4601.16686  -3953.21413  -6279.66785   1414.51098   1122.69737   2549.82138 
##          356          357          358          359          360          361 
##  -3901.69168   -682.55969    529.21909   6897.31115    273.56482    -35.65211 
##          362          363          364          365          366          367 
##   2586.24720  -2710.74367   -878.07550  -8752.65600  -4747.28488  -6374.01177 
##          368          369          370          371          372          373 
##  -5166.83058  -7500.78509   4709.01219    178.25297   6961.96254  -7674.01942 
##          374          375          376          377          378          379 
##  -2393.60410  -3525.75522  -2625.84170 -12621.82340   1599.87375 -10864.59756 
##          380          381          382          383          384          385 
##   5360.22619   9126.65393   3072.41097  -2401.54402   1572.12435   6739.02802 
##          386          387          388          389          390          391 
##  11496.18242  -5579.65005  -5253.10331   -137.47271   8576.50434   1936.91644 
##          392          393          394          395          396          397 
##  11347.09924  -9627.55808   2852.57195    812.01729    652.65631   -573.19771 
##          398          399          400          401          402          403 
##   -506.68758 -14450.17387   8367.10548  -1199.00616  -1401.92854   6940.11806 
##          404          405          406          407          408          409 
##  -7876.18152  -1349.51578  -2589.38257  -5896.55482  -2994.83482  -4064.27925 
##          410          411          412          413          414          415 
##  -8925.94120   5879.81822   1507.19970  -7462.32091  -7859.11680  13983.66534 
##          416          417          418          419          420          421 
##   3797.09593   4523.77866  -7951.69607  -4773.48166  -2682.62244   2721.86471 
##          422          423          424          425          426          427 
## -14053.77761  -3003.73820  -9310.53651   2720.33521   6770.37571   6491.57179 
##          428          429          430          431          432          433 
##  -3972.00404  -4154.84437  -4799.42735  -1911.23853  -5833.70180  -6799.38648 
##          434          435          436          437          438          439 
##  -6177.82954  -1660.32040  -1089.21913  -5187.62491   2340.42945   4671.04134 
##          440          441          442          443          444          445 
##  -5134.03324  -2284.87764   1445.23522  -3923.87278   2716.89989  -6637.84185 
##          446          447          448          449          450          451 
## -12241.50029  -4775.58888   9366.07082  -2138.93601   4642.88354  -5896.30180 
##          452          453          454          455          456          457 
##  -1215.32928    296.82003   2963.77182 -12275.42145   3208.74289  -6787.54840 
##          458          459          460          461          462          463 
##   6366.97031   2971.89024   2518.94161  -3795.67386   2092.16854     26.71116 
##          464          465          466          467          468          469 
##   1829.01859   -459.86792   3402.95579  -2543.27369   5860.10142  -6810.82111 
##          470          471          472          473          474          475 
##  -2934.19811  -2209.22510  -4686.74542   2924.57454   7784.02569  -5917.08717 
##          476          477          478          479          480          481 
##   1499.61221  -6136.87972  -2880.74153   1952.51061 -12945.01855  -9931.67043 
##          482          483          484          485          486          487 
##  -1475.91304   -222.47589  -1162.04733  -1517.91156  -9745.36697  10837.23133 
##          488          489          490          491          492          493 
##   6170.34368   7456.34897  -5298.06236   5422.54163   9420.59224   6295.67508 
##          494          495          496          497          498          499 
## -13176.81579 -10481.46791  -3499.88118  -1192.49399   -603.82228  -7691.47476 
##          500          501          502          503          504          505 
##    463.01868   4178.94668   5482.75489    721.84430    152.26857  -7166.18692 
##          506          507          508          509          510          511 
##    545.85893  -5051.01102   1772.90994  -1312.74531  -8178.95108   -719.63563 
##          512          513          514          515          516          517 
##  -2773.44794   -698.60184   1239.12897  -9547.15393  -7927.88568  24051.48635 
##          518          519          520          521          522          523 
##   9959.42938   6136.14512  -5026.74186   3001.40790  17240.17045  11900.34494 
##          524          525          526          527          528          529 
## -23620.08220  -4920.52801  -3672.65032   4582.99748   -277.64383 -11031.10313 
##          530          531          532          533          534          535 
##   4312.74904  13909.66523  -4781.89633   4482.34241   5714.62110  -1571.26404 
##          536          537          538          539          540          541 
##  -4368.77038  -6978.55711  -2104.19785   8300.56878    229.07228  -8043.25005 
##          542          543          544          545          546          547 
##   1800.42111   -579.23589    385.07767 -11000.38052 -11173.97577   1805.02169 
##          548          549          550          551          552          553 
##   6837.71578  -1356.52096    795.49768  -7735.27915   8458.51532    940.00568 
##          554          555          556          557          558          559 
## -11893.83995   9054.33575   8696.47973    256.65403   4998.17127  -3376.48797 
##          560          561          562          563          564          565 
##  14238.68852  21807.21503  -5906.64777  -9278.80389   6998.48444    516.42702 
##          566          567          568          569          570          571 
##   3714.68181  -7099.74593 -17159.42274   6569.34422   6454.41676   2015.96323 
##          572          573          574          575          576          577 
##   3231.75878   1936.54561  -1987.72302  14850.58948  -9327.38626  -6089.25481 
##          578          579          580          581          582          583 
##   8769.98130   3035.19697  -6345.80577   7602.09233  -3609.14703  -2652.98900 
##          584          585          586          587          588          589 
##  15780.62463 -14211.36907   8489.41598    246.32733  -6049.95427   -687.41228 
##          590          591          592          593          594          595 
##    310.16805 -10588.74630   1719.92414  -7174.96319   2956.45120   8823.54405 
##          596          597          598          599          600          601 
##  -7406.61454   5842.01023   2816.84850   6974.15768  -2984.69563   6294.39808 
##          602          603          604          605          606          607 
##  -8079.31246   2343.23483   1387.83854   3268.61003   1661.65271    572.85122 
##          608          609          610          611          612          613 
##  -5642.10759   8153.16749   -994.37621  -2414.50871  -3337.83793  -8162.53338 
##          614          615          616          617          618          619 
##  11916.60978   5076.24225  -9113.67406  11685.55432   6272.88375  -5284.71576 
##          620          621          622          623          624          625 
##  26546.18271 -12287.63514  -6475.49592   3341.55270  -3942.41769 -10448.04307 
##          626          627          628          629          630          631 
##  11289.30527 -21471.76035  -2559.38488   8531.00032  11145.43842  -1375.45204 
##          632          633          634          635          636          637 
##  33430.02290  -5984.57372   6161.05489   5876.63566  -1766.38696  -4928.02319 
##          638          639          640          641          642          643 
##  -1639.22091 -12183.53047  -2187.38516  -1855.72814  -2505.90878  -2866.29805 
##          644          645          646          647          648          649 
##   1782.46608   4442.48027  17051.01492  18878.19042   1436.52287   5292.77321 
##          650          651          652          653          654          655 
##  11127.56382  20758.85490   1571.10789 -27320.51831  -1075.72194  -2062.65540 
##          656          657          658          659          660          661 
##   2051.90986  -2988.04995 -10471.88887   1661.58556   4265.52941   -894.45749 
##          662          663          664          665          666          667 
##  13133.04699   1652.22599   2103.61662 -11395.80376   1484.41034   1316.73010 
##          668          669          670          671          672          673 
##  -5018.11718  -7337.54296   2041.79327  -3683.81614   2662.90826  -3330.09157 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17347.41 20140.20 24312.92 24036.27 26344.68 23728.29 24430.92 19754.11 
##       10       11       12       13       14       15       16       17 
## 19495.71 16889.22 17651.84 14443.71 14493.90 15145.86 16810.03 15162.14 
##       18       19       20       21       22       23       24       25 
## 16177.04 15562.54 22509.69 21611.20 21100.99 22954.90 22293.76 22933.84 
##       26       27       28       29       30       31       32       33 
## 24744.54 18788.91 20482.09 28169.70 28226.25 27905.41 25580.11 26955.75 
##       34       35       36       37       38       39       40       41 
## 30725.38 31067.15 32449.40 30007.57 34047.78 37194.58 34306.92 31178.73 
##       42       43       44       45       46       47       48       49 
## 30048.51 20808.01 28183.10 30573.04 31641.44 38348.91 37853.15 42426.27 
##       50       51       52       53       54       55       56       57 
## 46581.61 39419.02 34091.23 29209.02 22484.41 28654.26 25300.22 21668.55 
##       58       59       60       61       62       63       64       65 
## 25992.69 27228.18 27519.52 27923.47 23873.28 40147.60 41956.34 37293.12 
##       66       67       68       69       70       71       72       73 
## 41419.69 46241.43 56705.79 54757.51 40282.62 37836.13 40796.38 35205.41 
##       74       75       76       77       78       79       80       81 
## 30744.54 21625.58 24765.90 20769.45 22815.66 17808.71 19785.64 19026.94 
##       82       83       84       85       86       87       88       89 
## 18073.24 16179.06 17432.42 20971.55 25304.87 26275.83 26300.31 26905.26 
##       90       91       92       93       94       95       96       97 
## 30951.53 29804.43 30784.69 28886.10 28101.21 28462.32 28861.13 22561.29 
##       98       99      100      101      102      103      104      105 
## 25518.72 18682.35 17560.34 15638.50 15932.53 16596.97 20984.15 20085.17 
##      106      107      108      109      110      111      112      113 
## 23529.19 23323.17 24967.26 27789.06 25334.98 21846.41 22116.57 24712.28 
##      114      115      116      117      118      119      120      121 
## 35369.20 33596.57 35398.94 38339.97 40258.51 37991.10 32911.08 29322.37 
##      122      123      124      125      126      127      128      129 
## 31365.40 29678.22 30837.20 38291.74 37941.04 37020.51 33943.97 35691.14 
##      130      131      132      133      134      135      136      137 
## 40985.87 40436.86 31806.69 33047.32 36176.12 32655.36 31073.45 30175.94 
##      138      139      140      141      142      143      144      145 
## 26798.76 28186.16 27960.19 25690.71 27676.45 26327.48 20051.88 23024.49 
##      146      147      148      149      150      151      152      153 
## 20892.71 23812.93 24342.60 25902.60 26081.20 27703.74 28979.63 31953.83 
##      154      155      156      157      158      159      160      161 
## 27510.58 26787.62 24389.61 30171.04 41647.03 40009.18 37424.49 42348.81 
##      162      163      164      165      166      167      168      169 
## 43730.45 47081.06 42633.65 38030.54 43352.08 59342.66 61512.50 59956.91 
##      170      171      172      173      174      175      176      177 
## 56843.28 55273.13 57924.04 56966.79 49406.25 52175.78 55846.86 55882.36 
##      178      179      180      181      182      183      184      185 
## 62874.93 53558.77 50371.82 41356.18 33057.26 36498.45 46408.79 45875.20 
##      186      187      188      189      190      191      192      193 
## 51714.17 57349.33 67926.19 73108.06 66923.28 67113.01 74048.17 69807.26 
##      194      195      196      197      198      199      200      201 
## 65415.67 54865.13 49009.39 50406.46 46085.38 38403.89 44705.82 42967.10 
##      202      203      204      205      206      207      208      209 
## 42611.99 43081.19 49745.32 58464.61 58101.29 59793.13 61417.26 65135.97 
##      210      211      212      213      214      215      216      217 
## 74421.52 66655.69 55070.54 49782.41 40950.88 37966.76 41020.90 31231.87 
##      218      219      220      221      222      223      224      225 
## 47880.89 55061.72 55946.38 78276.58 85628.30 87593.23 95039.45 86163.36 
##      226      227      228      229      230      231      232      233 
## 80276.52 79866.45 76580.47 75757.99 80404.44 81742.63 76284.48 71588.94 
##      234      235      236      237      238      239      240      241 
## 77134.50 64037.58 56226.33 48330.87 40102.82 44214.58 46326.16 39902.07 
##      242      243      244      245      246      247      248      249 
## 33702.48 43788.35 38145.24 42005.69 34417.48 33148.08 36733.48 39503.38 
##      250      251      252      253      254      255      256      257 
## 30508.18 36332.71 40061.45 45156.90 47823.24 47325.52 57429.51 74592.16 
##      258      259      260      261      262      263      264      265 
## 74410.75 67849.31 69302.81 65595.86 67014.35 60891.39 50371.08 46474.40 
##      266      267      268      269      270      271      272      273 
## 46679.80 42860.97 51496.77 47841.11 51908.56 50061.14 54050.74 54341.09 
##      274      275      276      277      278      279      280      281 
## 60243.47 57922.94 67410.96 61451.91 61657.92 60038.56 65671.63 59521.99 
##      282      283      284      285      286      287      288      289 
## 56151.54 45903.60 44331.00 61233.77 66658.14 67059.77 64526.73 63631.54 
##      290      291      292      293      294      295      296      297 
## 67556.02 71391.50 52751.35 43042.63 37168.16 47292.22 50472.07 49602.96 
##      298      299      300      301      302      303      304      305 
## 73336.72 79168.47 79823.36 84347.64 82580.65 77705.91 81106.47 56485.11 
##      306      307      308      309      310      311      312      313 
## 52817.70 52503.59 46412.46 43675.24 47209.72 39904.27 38650.79 33316.28 
##      314      315      316      317      318      319      320      321 
## 37028.49 36225.48 39983.72 38006.67 63300.18 61182.60 62775.16 70620.43 
##      322      323      324      325      326      327      328      329 
## 72962.14 97956.85 96365.52 72657.59 71478.41 69867.12 61972.64 59149.86 
##      330      331      332      333      334      335      336      337 
## 29672.49 33289.59 33720.72 35996.33 35349.75 41006.58 42061.63 37400.00 
##      338      339      340      341      342      343      344      345 
## 36629.43 36753.68 32162.07 38046.45 38697.19 38950.70 39809.77 41561.59 
##      346      347      348      349      350      351      352      353 
## 43348.78 43105.18 36184.59 26932.58 32175.17 31057.93 30655.81 28317.77 
##      354      355      356      357      358      359      360      361 
## 32907.30 36589.89 40968.26 39191.85 40428.07 42525.69 49779.72 50319.79 
##      362      363      364      365      366      367      368      369 
## 50517.61 52933.74 50465.22 49920.37 42706.00 39956.30 36206.26 34027.36 
##      370      371      372      373      374      375      376      377 
## 30160.42 37309.18 39552.47 47287.45 41374.18 40831.90 39397.13 38938.82 
##      378      379      380      381      382      383      384      385 
## 29980.84 34491.17 27675.49 35737.92 45873.73 49371.12 47677.45 49631.11 
##      386      387      388      389      390      391      392      393 
## 55732.53 65036.94 58377.82 52951.62 52685.50 59924.23 60437.62 68940.84 
##      394      395      396      397      398      399      400      401 
## 58254.43 59791.41 59359.92 58853.63 57369.40 56154.60 43165.89 51587.72 
##      402      403      404      405      406      407      408      409 
## 50607.21 49593.17 55872.32 48557.09 47881.38 46239.98 41999.69 40852.71 
##      410      411      412      413      414      415      416      417 
## 38953.51 33160.32 40882.94 43753.46 38527.40 33709.33 48297.33 52068.79 
##      418      419      420      421      422      423      424      425 
## 55923.12 48535.91 44929.34 43630.56 47148.63 35788.60 35522.97 29891.24 
##      426      427      428      429      430      431      432      433 
## 35374.48 43543.29 50304.00 47131.13 44255.71 41239.52 41129.84 37674.82 
##      434      435      436      437      438      439      440      441 
## 33886.83 31173.61 32719.65 34533.77 32576.43 37349.82 43437.03 40251.31 
##      442      443      444      445      446      447      448      449 
## 39962.91 42912.02 40838.39 44751.84 40089.36 31292.59 30152.21 41292.65 
##      450      451      452      453      454      455      456      457 
## 40980.26 46523.73 42243.04 42586.04 44175.66 47822.99 37890.26 42647.12 
##      458      459      460      461      462      463      464      465 
## 38157.60 45582.40 49035.34 51605.96 48397.83 50694.00 50891.70 52605.44 
##      466      467      468      469      470      471      472      473 
## 52112.62 55000.27 52379.47 57334.39 50722.77 48379.23 46992.32 43681.00 
##      474      475      476      477      478      479      480      481 
## 47365.55 54686.66 49219.82 50890.59 45778.74 44188.63 46967.59 36583.53 
##      482      483      484      485      486      487      488      489 
## 30267.77 32101.48 34746.76 36208.34 37155.80 30917.77 43209.23 49742.51 
##      490      491      492      493      494      495      496      497 
## 56442.63 51254.89 55995.84 63484.04 67222.82 53741.04 44498.45 42561.07 
##      498      499      500      501      502      503      504      505 
## 42878.11 43654.19 38245.98 40599.20 45799.67 51373.01 52069.16 52177.62 
##      506      507      508      509      510      511      512      513 
## 45999.57 47314.01 43644.52 46347.46 46019.52 39855.06 40964.59 40155.46 
##      514      515      516      517      518      519      520      521 
## 41240.01 43829.73 36806.31 32175.66 55610.00 63615.14 67198.46 60703.73 
##      522      523      524      525      526      527      528      529 
## 62017.69 75344.37 82188.08 57615.81 52583.65 49341.00 53636.50 53152.25 
##      530      531      532      533      534      535      536      537 
## 43522.97 48419.62 60838.75 55464.09 58796.95 62708.69 59817.48 54942.99 
##      538      539      540      541      542      543      544      545 
## 48529.91 47211.43 54997.21 54752.39 47454.29 49635.52 49465.49 50146.09 
##      546      547      548      549      550      551      552      553 
## 40973.40 32964.84 37223.86 45185.66 44986.50 46659.85 40783.91 49624.99 
##      554      555      556      557      558      559      560      561 
## 50758.27 40732.38 50091.38 57804.20 57181.26 60710.35 56558.31 68094.50 
##      562      563      564      565      566      567      568      569 
## 84464.79 74744.80 63526.52 67861.43 66021.60 67185.60 58916.42 43210.94 
##      570      571      572      573      574      575      576      577 
## 50085.87 55878.32 57038.53 59074.45 59709.15 56890.41 68903.39 58479.54 
##      578      579      580      581      582      583      584      585 
## 52322.30 59778.80 61254.09 54479.91 60626.86 56287.42 53388.38 66699.51 
##      586      587      588      589      590      591      592      593 
## 52406.16 59610.24 58719.95 52561.98 51880.40 52151.17 43044.22 45787.68 
##      594      595      596      597      598      599      600      601 
## 40516.69 44681.46 53277.47 46735.99 52483.15 54815.56 60376.41 56607.89 
##      602      603      604      605      606      607      608      609 
## 61329.74 53059.34 54903.45 55664.96 57929.06 58492.15 58041.68 52330.26 
##      610      611      612      613      614      615      616      617 
## 59257.09 57354.22 54506.84 51275.82 44373.10 55663.61 59476.82 50585.30 
##      618      619      620      621      622      623      624      625 
## 60788.69 64893.72 58507.82 80310.92 65717.78 58193.59 60158.27 55600.33 
##      626      627      628      629      630      631      632      633 
## 46120.27 56623.19 37550.81 37413.71 46799.28 57081.74 55163.69 83344.00 
##      634      635      636      637      638      639      640      641 
## 73717.66 75876.36 77482.39 72309.45 65167.79 61866.39 50002.39 48401.87 
##      642      643      644      645      646      647      648      649 
## 47314.62 45825.87 44241.39 46867.09 51396.27 66081.10 80229.76 77408.08 
##      650      651      652      653      654      655      656      657 
## 78294.58 84053.86 97241.61 92100.38 62938.58 60439.08 57451.66 58417.48 
##      658      659      660      661      662      663      664      665 
## 54926.46 45522.41 47861.18 52096.46 51304.10 62644.92 62524.95 62808.95 
##      666      667      668      669      670      671      672      673 
## 51485.02 52818.56 53817.55 49245.40 43340.21 46317.10 43961.81 47381.95 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8255
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    8.173173  0.5325147    3.637891
## t2* 1838.738763 23.0052269  218.990087
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.500747       8.284576   15.39597
## 2    lag_depvar 1524.051170    1848.723615 2245.32546

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Feb 19 01:44:29 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Feb 19 01:44:36 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Feb 19 01:44:42 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Feb 19 01:44:49 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Feb 19 01:44:56 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Feb 19 01:45:02 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Feb 19 01:45:09 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Feb 19 01:45:16 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Feb 19 01:45:22 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Feb 19 01:45:29 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 5.195333 5.410333 5.629750 6.4634898
Comida 366.009167 310.278417 314.087500 347.4559184
Comunicaciones 0.000000 0.000000 0.000000 0.0000000
Electricidad 38.104750 47.072333 38.297667 35.4057347
Enceres 18.259750 20.086417 17.443792 22.5696327
Farmacia 4.733250 1.831667 7.913875 8.4729184
Gas/Bencina 35.219333 44.325000 28.954333 27.0333878
Diosi 55.804250 31.180667 41.934250 43.2965102
donaciones/regalos 0.000000 0.000000 7.170083 5.6065102
Electrodomésticos/ Mantención casa 0.000000 3.944000 30.269500 18.1524082
VTR 12.829167 25.156667 22.121792 19.1066939
Netflix 4.555500 7.151583 7.090167 6.7775714
Otros 0.000000 3.151083 1.575542 0.7716939
Total 540.710500 499.588167 522.488250 541.1124694
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2191, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-12-31 00:04:58 sería de: 37.564 pesos// Percentil 95% más alto proyectado: 40.778,21

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36833.92 36828.51
Lo.80 36883.04 36886.39
Point.Forecast 37564.03 39253.82
Hi.80 39365.36 43947.75
Hi.95 40353.63 46432.57


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2560  1018.5601
## s.e.  0.1297    28.7097
## 
## sigma^2 = 28486:  log likelihood = -391.87
## AIC=789.74   AICc=790.17   BIC=796.02
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept    xreg
##       0.2362   835.0084  6.1375
## s.e.  0.1436   294.8750  9.4651
## 
## sigma^2 = 27183:  log likelihood = -377.92
## AIC=763.85   AICc=764.6   BIC=772.09
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 749.5908 676.3560 750.3598
Lo.80 864.6994 794.8049 834.0495
Point.Forecast 1082.1447 1018.5601 1021.9135
Hi.80 1299.5900 1242.3152 1264.5914
Hi.95 1414.6985 1360.7641 1415.5848


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.8      
##  [7] tidytext_0.4.1      DT_0.31             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.4       xts_0.13.2         
## [13] forecast_8.21.1     wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.4       lubridate_1.9.3     forcats_1.0.0      
## [22] dplyr_1.1.4         purrr_1.0.1         tidyr_1.3.1        
## [25] tibble_3.2.1        ggplot2_3.4.4       tidyverse_2.0.0    
## [28] sjPlot_2.8.15       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-4       sparklyr_1.8.4      httr_1.4.7         
## [34] readxl_1.4.3        zoo_1.8-12          stringr_1.5.1      
## [37] stringi_1.8.3       data.table_1.15.0   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.5        
##  [16] askpass_1.1         timechange_0.3.0    anytime_0.3.9      
##  [19] tseries_0.10-55     colorspace_2.1-0    xfun_0.39          
##  [22] crayon_1.5.2        jsonlite_1.8.4      lme4_1.1-35.1      
##  [25] glue_1.6.2          gtable_0.3.4        emmeans_1.10.0     
##  [28] sjstats_0.18.2      sjmisc_2.8.9        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           ggeffects_1.4.0     Rcpp_1.0.10        
##  [37] viridisLite_0.4.2   xtable_1.8-4        performance_0.10.9 
##  [40] bit_4.0.5           htmlwidgets_1.6.2   timeSeries_4032.109
##  [43] gplots_3.1.3.1      ellipsis_0.3.2      spatial_7.3-14     
##  [46] pkgconfig_2.0.3     farver_2.1.1        nnet_7.3-16        
##  [49] sass_0.4.5          dbplyr_2.4.0        janitor_2.2.0      
##  [52] utf8_1.2.3          tidyselect_1.2.0    labeling_0.4.3     
##  [55] rlang_1.1.3         munsell_0.5.0       cellranger_1.1.0   
##  [58] tools_4.1.2         cachem_1.0.7        cli_3.6.1          
##  [61] generics_0.1.3      sjlabelled_1.2.0    broom_1.0.5        
##  [64] evaluate_0.20       fastmap_1.1.1       yaml_2.3.7         
##  [67] knitr_1.45          bit64_4.0.5         caTools_1.18.2     
##  [70] nlme_3.1-153        slam_0.1-50         xml2_1.3.3         
##  [73] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.14    
##  [76] curl_5.2.0          bslib_0.4.2         highr_0.10         
##  [79] fBasics_4032.96     Matrix_1.6-5        its.analysis_1.6.0 
##  [82] nloptr_2.0.3        urca_1.3-3          vctrs_0.6.5        
##  [85] pillar_1.9.0        lifecycle_1.0.3     lmtest_0.9-40      
##  [88] jquerylib_0.1.4     estimability_1.4.1  bitops_1.0-7       
##  [91] insight_0.19.8      R6_2.5.1            KernSmooth_2.23-20 
##  [94] janeaustenr_1.0.0   codetools_0.2-18    assertthat_0.2.1   
##  [97] boot_1.3-28         MASS_7.3-54         gtools_3.9.5       
## [100] openssl_2.0.6       withr_3.0.0         fracdiff_1.5-3     
## [103] bayestestR_0.13.2   parallel_4.1.2      hms_1.1.3          
## [106] quadprog_1.5-8      timeDate_4032.109   minqa_1.2.6        
## [109] snakecase_0.11.1    rmarkdown_2.25      carData_3.0-5      
## [112] TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))